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Benjamine Sarton

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4 papers
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4

YNIMG Journal 2026 Journal Article

Brain dynamic organization at the acute stage of severe brain injury

  • Gabriel Della Bella
  • Benjamine Sarton
  • Giulia Maria Mattia
  • Patrice Peran
  • Walter Lamberti
  • Pablo Barttfeld
  • Stein Silva

Acute Disorders of consciousness (DoC) poses significant clinical challenges, including early and accurate prognostication of neurological outcomes. Current assessment tools are limited in their predictive power, leaving many patients in a "gray zone" of uncertainty. While acute DoC are traditionally associated with structural brain damage, emerging evidence suggests that they are primarily driven by a withdrawal of excitatory synaptic activity across key cortical and subcortical regions, which can be captured through the dynamic analysis of resting-state brain activity. This study investigates the temporal dynamics of brain connectivity, shortly after severe brain injury (average of 13.9 days from onset), hypothesizing that acute DoC is marked by a global reorganization of functional connectivity and a shift toward less informative brain states, with distinct patterns emerging based on the underlying injury mechanism. Using functional magnetic resonance imaging (fMRI), we identify six distinct brain states across severely brain injured patients and healthy controls. These states, when sorted by decreasing entropy, span a continuum from state 1, characterized by high entropy, widespread positive long-distance coordination, and high global connectivity, predominantly observed in healthy controls, to state 6, which exhibits low entropy and minimal functional connectivity, and is predominantly associated with acute DoC. We demonstrate that the probability of occurrence of the more complex brain state correlates with improved neurological recovery at 3 months, as assessed by the Coma Recovery Scale-Revised (CRS-R). Hence, we were able to train a classifier based on brain state dynamics that achieved an accuracy of 78.5% in predicting patients' recovery potential (AUC = 0.864). Overall, our findings suggest that dynamic brain connectivity, particularly the entropy of brain states, can be a reliable early predictor of recovery from acute DoC, bridging the divide between theoretical advances and bedside medical decision-making.

YNICL Journal 2024 Journal Article

Translocator protein (TSPO) genotype does not change cerebrospinal fluid levels of glial activation, axonal and synaptic damage markers in early Alzheimer’s disease

  • Dominique Gouilly
  • Agathe Vrillon
  • Elsa Bertrand
  • Marie Goubeaud
  • Hélène Catala
  • Johanne Germain
  • Nadéra Ainaoui
  • Marie Rafiq

BACKGROUND: PET imaging of the translocator protein (TSPO) is used to assess in vivo brain inflammation. One of the main methodological issues with this method is the allelic dependence of the radiotracer affinity. In Alzheimer's disease (AD), previous studies have shown similar clinical and patho-biological profiles between TSPO genetic subgroups. However, there is no evidence regarding the effect of the TSPO genotype on cerebrospinal-fluid biomarkers of glial activation, and synaptic and axonal damage. METHOD: We performed a trans-sectional study in early AD to compare cerebrospinal-fluid levels of GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin between TSPO genetic subgroups. RESULTS: We recruited 33 patients with early AD including 16 (48%) high affinity binders, 13 (39%) mixed affinity binders, and 4/33 (12%) low affinity binders. No difference was observed in terms of demographics, and cerebrospinal fluid levels of each biomarker for the different subgroups. CONCLUSION: TSPO genotype is not associated with a change in glial activation, synaptic and axonal damage in early AD. Further studies with larger numbers of participants will be needed to confirm that the inclusion of specific TSPO genetic subgroups does not introduce selection bias in studies and trials of AD that combine TSPO imaging with cerebrospinal fluid biomarkers.

YNICL Journal 2017 Journal Article

Neural signature of coma revealed by posteromedial cortex connection density analysis

  • Briguita Malagurski
  • Patrice Péran
  • Benjamine Sarton
  • Beatrice Riu
  • Leslie Gonzalez
  • Fanny Vardon-Bounes
  • Thierry Seguin
  • Thomas Geeraerts

Posteromedial cortex (PMC) is a highly segregated and dynamic core, which appears to play a critical role in internally/externally directed cognitive processes, including conscious awareness. Nevertheless, neuroimaging studies on acquired disorders of consciousness, have traditionally explored PMC as a homogenous and indivisible structure. We suggest that a fine-grained description of intrinsic PMC topology during coma, could expand our understanding about how this cortical hub contributes to consciousness generation and maintain, and could permit the identification of specific markers related to brain injury mechanism and useful for neurological prognostication. To explore this, we used a recently developed voxel-based unbiased approach, named functional connectivity density (CD). We compared 27 comatose patients (15 traumatic and 12 anoxic), to 14 age-matched healthy controls. The patients' outcome was assessed 3 months later using Coma Recovery Scale-Revised (CRS-R). A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation) voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795-1). These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.